Finding the QRS Complex in a Sampled ECG Signal Using AI Methods

Sammanfattning

This study aimed to explore the application of artificial intelligence (AI) and machine learning (ML) techniques in implementing a QRS detector forambulatory electrocardiography (ECG) monitoring devices. Three ML models, namely long short-term memory (LSTM), convolutional neural network (CNN), and multilayer perceptron (MLP), were compared and evaluated using the MIT-BIH arrhythmia database (MITDB) and the MIT-BIH noise stress test database (NSTDB). The MLP model consistently outperformed the other models, achieving high accuracy in R-peak detection. However, when tested on noisy data, all models faced challenges in accurately predicting R-peaks, indicating the need for further improvement. To address this, the study emphasized the importance of iteratively refining the input data configurations for achieving accurate R-peak detection. By incorporating both the MITDB and NSTDB during training, the models demonstrated improved generalization to noisy signals. This iterative refinement process allowed for the identification of the best models and configurations, consistently surpassing existing ML-based implementations and outperforming the current ECG analysis system. The MLP model, without shifting segments and utilizing both datasets, achieved an outstanding accuracy of 99.73 % in R-peak detection. This accuracy exceeded values reported in the literature, demonstrating the superior performance of this approach. Furthermore, the shifted MLP model, which considered temporal dependencies by incorporating shifted segments, showed promising results with an accuracy of 99.75 %. It exhibited enhanced accuracy, precision, and F1-score compared to the other models, highlighting the effectiveness of incorporating shifted segments. For future research, it is important to address challenges such as overfitting and validate the models on independent datasets. Additionally, continuous refinement and optimization of the input data configurations will contribute to further advancements in ECG signal analysis and improve the accuracy of R-peak detection. This study underscores the potential of ML techniques in enhancing ECG analysis, ultimately leading to improved cardiac diagnostics and better patient care.